Do Hypoalbuminaemia Increase the Risk of Surgical Site Infection in Neck of Femur Fracture Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Serum albumin plays an important role in physiological and inflammatory haemostasis, and low serum levels are linked with an increased incidence of surgical site infections (SSI). Although this has been demonstrated in the spine and elective arthroplasty settings, there is a paucity of evidence with regard to the effect of low serum albumin on rates of SSI following surgery for adult patients suffering from traumatic and acute hip fractures. A systematic review was conducted using the PRISMA guidelines. Four databases were searched for randomised controlled trials (RCTs), cohort studies, and case-controlled studies. The risk of bias was assessed using the Newcastle-Ottawa Score (NOS). Data was collected and pooled using RevMan Web software. Results were reported as odds ratios (OR) with 95% confidence intervals (CI) and statistical significance of p <0.05. An inverse variance model was used in the meta-analysis. Six retrospective studies (five cohorts and one case-control) with a total of 43,059 patients were included. 45.3% (n=19 496) had low serum albumin (<3.5 g/dL). Hypoalbuminemia was associated with a significantly higher risk of any form of SSI (OR 1.25, p=0.008) and deep SSI (OR 1.76, p=0.05). There was no statistical significance between hypoalbuminemia and the incidence of superficial SSI (OR 1.06, p=0.77). Organ-space SSI was associated with hypoalbuminemia, although one study reported this with poor statistical significance (OR 8.74, p<0.054). Hypoalbuminemia increases the risk of most forms of surgical site infections, both superficial and deep. There is a weak conclusion to draw between the incidence of deep-space organ infections and low serum albumin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".